Publication:
Facebook Social Media for Depression Detection in the Thai Community

dc.contributor.authorKantinee Katchapakirinen_US
dc.contributor.authorKonlakorn Wongpatikasereeen_US
dc.contributor.authorPanida Yomabooten_US
dc.contributor.authorYongyos Kaewpitakkunen_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherTelenor Groupen_US
dc.contributor.otherTOT Public Company Limiteden_US
dc.date.accessioned2019-08-23T10:55:12Z
dc.date.available2019-08-23T10:55:12Z
dc.date.issued2018-09-06en_US
dc.description.abstract© 2018 IEEE. Depression is one of the leading mental health problems. It is a cause of psychological disability and economic burden to a country. Around 1.5 Thai people suffer from depression and its prevalence has been growing up fast. Although it is a serious psychological problem, less than a half of those who have this emotional problem gained access to mental health service. This could be a result of many factors including having lack awareness about the disease. One of the solutions would be providing a tool that depression could be easily and early detected. This would help people to be aware of their emotional states and seek help from professional services. Given Facebook is the most popular social network platform in Thailand, it could be a largescale resource to develop a depression detection tool. This research employs Natural Language Processing (NLP) techniques to develop a depression detection algorithm for the Thai language on Facebook where people use it as a tool for sharing opinions, feelings, and life events. Results from 35 Facebook users indicated that Facebook behaviours could predict depression level.en_US
dc.identifier.citationProceeding of 2018 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018. (2018)en_US
dc.identifier.doi10.1109/JCSSE.2018.8457362en_US
dc.identifier.other2-s2.0-85057741064en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/45586
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85057741064&origin=inwarden_US
dc.subjectComputer Scienceen_US
dc.titleFacebook Social Media for Depression Detection in the Thai Communityen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85057741064&origin=inwarden_US

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